feels like an insult to readers to try to pretend that their revenue per month is comparable to google or apples growth when the funding is absurdly different, not to mention inflation itself.

I am very much onboard with AI within my workflow. I just don't really see a future where openai/anthropic are the absolute front runners for devs though. Maybe OpenAI does just have the better vision by targeting the general public instead, and just competing to become the next google before google can just stay google?

What is their next step to ensure local models never overtake them? If i could use opus 4.6 as a local model isntead and wrap it in someone else's cli tool, i 100% do it today. are the future model's gonna be so far beyond in capability that this sounds foolish? the top models are more than enough to keep up with my own features before i can think of more... so how do they stretch further than that?

A side note i keep thinking about, how impossible is a world where open source base models are collectively trained similar to a proof of work style pool, and then smaller companies simply spin off their own finishing touches or whatever based on that base model? am i thinking of thinks too simplistically? is this not a possibility?

Anthropic is definitely gaining ground over OpenAI in the business world. Cowork is the absolute hotness right now, and even prompted MSFT to drop their own variant yesterday

> how impossible is a world where open source base models are collectively trained similar to a proof of work style pool

Current multi-GPU training setups assume much higher bandwidth (and lower latency) between the GPUs than you can get with an internet connection. Even cross-datacenter training isn't really practical.

LLM training isn't embarrassingly parallel, not like crypto mining is for example. It's not like you can just add more nodes to the mix and magically get speedups. You can get a lot out of parallelism, certainly, but it's not as straightforward and requires work to fully utilize.

It's hard to train models in the open. All the big players are using lots of "dodgy" training data. Like books, video, code, destinations. If you did that in the open, the lawyers would shut you down.

Though I think these companies are wildly overvalued, I don't see LLMs as a service going away in the future. The value in OpenAI is that it provides extra compute, data access, etc. My money is on local AI becoming more of a thing, while services like OpenAI still exist for local AIs to consult with. If a local model can somehow know that it's out of it's depth on a question/prompt, it can ask an OpenAI model if it's available, but otherwise still work locally if OpenAI fails to respond or goes out of business. To me that makes a lot more sense than the future being either-or.

> What is their next step to ensure local models never overtake them?

As someone who experiments with local models a lot, I don’t see this as a threat. Running LLMs on big server hardware will always be faster and higher quality than what we can fit on our laptops.

Even in the future when there are open weight models that I can run on my laptop that match today’s Opus, I would still be using a hosted variant for most work because it will be faster, higher quality, and not make my laptop or GPU turn into a furnace every time I run a query.

You can host a website on any rackmount server for pennies compared to AWS. But people still use AWS.

The market for local models is always gonna be a small niche, primarily for the paranoid.